Peter Galan is a (retired) control system designer with extensive experience in electronics, control systems and software design. He worked for many companies like ZTS, GE, Husky, Nortel, JDSU, Emerson (in Canada and the U.S.A.) and previously at the Technical University in Kosice (former Czechoslovakia). He holds a Ph.D. degree in Automated Control Systems and M.Eng degree in Applied Cybernetics from Czech Technical University in Prague.
Control system designers consistently seek the best control method for an application. See examples, equations and graphics.
The intelligent battery pack can be made safer by using soft computing techniques to make process variables more reliable and consistent.
The analog PID controller, still considered as the most powerful, can be modified as a discrete-time control system. Equations and examples follow.
Practical applications of artificial neural networks (ANNs) for control systems, especially for non-linear systems, include simulating time-optimal controllers and for ANN-based controlled system (plant) models. Such models, combined with classical proportional-integral-derivative (PID) controllers, can enable adaptive and other, more sophisticated, control systems.
C code is provided and explained for creating event-driven applications for embedded systems, and simulating a task-manager application.
Get help for finite-state machine programming for embedded systems using C programming language.
Control methods that can be more effective than proportional-integral-derivative (PID) controllers, include feed-forward control, disturbance compensation, adaptive control, optimal PID control and fuzzy control.
Cover Story: While simulation systems can help for control system programming design, a general-purpose programming language like C# can be used: First, some basic control system theory.